23 research outputs found
An Adaptive Color Image Segmentation
A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also
An Adaptive Color Image Segmentation
A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also
A methodology for energy performance classification of residential building stock of Hamirpur
Not Available
Not AvailableSafflower is a traditional oilseed crop in the world. Its seed oil is a healthy edible oil containing high
amount of unsaturated fatty acids. Genetically diverse exotic cultivars are valuable germplasmfor introducing
new diversity in safflower improvement programmes. In this study, we characterized safflower
cultivars of India (30) andMexico (23) comprising varieties, hybrids and advanced lines developed over
50 years for genetic distinctiveness using 38 simple sequence repeat (SSR) loci. Genetic diversity estimates
across cultivar groups (total, India and Mexico) were as follows: mean number of alleles (3.2,
3.1, 2.6), expected heterozygosity (0.42, 0.37, 0.37) and polymorphism information content (0.36,
0.33, 0.32) respectively, which suggested narrow SSR allelic diversity within and between cultivar
groups. However, distance-based cluster analysis (neighbour-joining tree) and model-based
STRUCTURE analysis revealed that safflower cultivars of India and Mexico, with the exception of a
few, formtwo genetically distinct groups. High level of genetic variation explained between the populations
(40%) and Fst estimate (0.4) suggested that the cultivar groups were highly differentiated with limited
gene flow supporting a strong genetic structuring. High oil *38% and high oleic (73–79%)
contents of a subset of Mexican safflower varieties and advanced lines were confirmed in field trials
in India. These exotic sources from Mexico are valuable for safflower breeding programmes in India
to develop new cultivars with high oil yielding potential and high oleic acid content,which is the current
market demand.Not Availabl